Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations11113
Missing cells101546
Missing cells (%)25.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory281.0 B

Variable types

Numeric3
Boolean1
Text29
Categorical2
DateTime1

Alerts

current is highly overall correlated with height and 1 other fieldsHigh correlation
height is highly overall correlated with currentHigh correlation
weight is highly overall correlated with currentHigh correlation
current is highly imbalanced (99.6%) Imbalance
function is highly imbalanced (87.6%) Imbalance
birth_place has 2386 (21.5%) missing values Missing
birth_country has 1638 (14.7%) missing values Missing
residence_place has 4309 (38.8%) missing values Missing
residence_country has 2825 (25.4%) missing values Missing
nickname has 8147 (73.3%) missing values Missing
hobbies has 6906 (62.1%) missing values Missing
occupation has 1529 (13.8%) missing values Missing
education has 5575 (50.2%) missing values Missing
family has 5552 (50.0%) missing values Missing
lang has 508 (4.6%) missing values Missing
coach has 2891 (26.0%) missing values Missing
reason has 5267 (47.4%) missing values Missing
hero has 7798 (70.2%) missing values Missing
influence has 8958 (80.6%) missing values Missing
philosophy has 8330 (75.0%) missing values Missing
sporting_relatives has 8595 (77.3%) missing values Missing
ritual has 10256 (92.3%) missing values Missing
other_sports has 10053 (90.5%) missing values Missing
code has unique values Unique
height has 6032 (54.3%) zeros Zeros
weight has 10792 (97.1%) zeros Zeros

Reproduction

Analysis started2025-03-12 19:35:54.918777
Analysis finished2025-03-12 19:35:56.689834
Duration1.77 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

code
Real number (ℝ)

Unique 

Distinct11113
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1887418.3
Minimum1532872
Maximum9460001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:56.712330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1532872
5-th percentile1543020.6
Q11888184
median1918840
Q31948950
95-th percentile1976498.2
Maximum9460001
Range7927129
Interquartile range (IQR)60766

Descriptive statistics

Standard deviation358800.59
Coefficient of variation (CV)0.19010125
Kurtosis60.717461
Mean1887418.3
Median Absolute Deviation (MAD)30453
Skewness6.4659513
Sum2.097488 × 1010
Variance1.2873786 × 1011
MonotonicityNot monotonic
2025-03-12T16:35:56.746395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1532872 1
 
< 0.1%
1940310 1
 
< 0.1%
1940262 1
 
< 0.1%
1940263 1
 
< 0.1%
1940264 1
 
< 0.1%
1940265 1
 
< 0.1%
1940266 1
 
< 0.1%
1940267 1
 
< 0.1%
1940300 1
 
< 0.1%
1940308 1
 
< 0.1%
Other values (11103) 11103
99.9%
ValueCountFrequency (%)
1532872 1
< 0.1%
1532873 1
< 0.1%
1532874 1
< 0.1%
1532944 1
< 0.1%
1532945 1
< 0.1%
1532951 1
< 0.1%
1533112 1
< 0.1%
1533136 1
< 0.1%
1533176 1
< 0.1%
1533188 1
< 0.1%
ValueCountFrequency (%)
9460001 1
< 0.1%
4986655 1
< 0.1%
4983537 1
< 0.1%
4982762 1
< 0.1%
4982175 1
< 0.1%
4980004 1
< 0.1%
4979790 1
< 0.1%
4979624 1
< 0.1%
4979566 1
< 0.1%
4979565 1
< 0.1%

current
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
True
11110 
False
 
3
ValueCountFrequency (%)
True 11110
> 99.9%
False 3
 
< 0.1%
2025-03-12T16:35:56.766115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

name
Text

Distinct11103
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:56.835455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length31
Mean length14.70818
Min length3

Characters and Unicode

Total characters163452
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11093 ?
Unique (%)99.8%

Sample

1st rowALEKSANYAN Artur
2nd rowAMOYAN Malkhas
3rd rowGALSTYAN Slavik
4th rowHARUTYUNYAN Arsen
5th rowTEVANYAN Vazgen
ValueCountFrequency (%)
de 105
 
0.4%
van 97
 
0.4%
maria 77
 
0.3%
mohamed 49
 
0.2%
laura 47
 
0.2%
lee 43
 
0.2%
anna 42
 
0.2%
daniel 42
 
0.2%
wang 42
 
0.2%
thomas 41
 
0.2%
Other values (14854) 23640
97.6%
2025-03-12T16:35:56.945550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13112
 
8.0%
A 11091
 
6.8%
a 10383
 
6.4%
E 7882
 
4.8%
i 6707
 
4.1%
e 6330
 
3.9%
R 6078
 
3.7%
O 6036
 
3.7%
I 6020
 
3.7%
N 5847
 
3.6%
Other values (52) 83966
51.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 163452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13112
 
8.0%
A 11091
 
6.8%
a 10383
 
6.4%
E 7882
 
4.8%
i 6707
 
4.1%
e 6330
 
3.9%
R 6078
 
3.7%
O 6036
 
3.7%
I 6020
 
3.7%
N 5847
 
3.6%
Other values (52) 83966
51.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 163452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13112
 
8.0%
A 11091
 
6.8%
a 10383
 
6.4%
E 7882
 
4.8%
i 6707
 
4.1%
e 6330
 
3.9%
R 6078
 
3.7%
O 6036
 
3.7%
I 6020
 
3.7%
N 5847
 
3.6%
Other values (52) 83966
51.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 163452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13112
 
8.0%
A 11091
 
6.8%
a 10383
 
6.4%
E 7882
 
4.8%
i 6707
 
4.1%
e 6330
 
3.9%
R 6078
 
3.7%
O 6036
 
3.7%
I 6020
 
3.7%
N 5847
 
3.6%
Other values (52) 83966
51.4%
Distinct10756
Distinct (%)96.8%
Missing3
Missing (%)< 0.1%
Memory size86.9 KiB
2025-03-12T16:35:57.039819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length15
Mean length9.260216
Min length3

Characters and Unicode

Total characters102881
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10499 ?
Unique (%)94.5%

Sample

1st rowALEKSANYAN A
2nd rowAMOYAN M
3rd rowGALSTYAN S
4th rowHARUTYUNYAN A
5th rowTEVANYAN V
ValueCountFrequency (%)
m 1116
 
4.8%
a 1104
 
4.8%
s 754
 
3.3%
j 711
 
3.1%
l 550
 
2.4%
e 469
 
2.0%
k 461
 
2.0%
c 461
 
2.0%
t 444
 
1.9%
d 426
 
1.9%
Other values (9590) 16531
71.8%
2025-03-12T16:35:57.160029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11921
 
11.6%
A 11033
 
10.7%
E 7851
 
7.6%
R 6058
 
5.9%
O 6013
 
5.8%
I 5990
 
5.8%
N 5827
 
5.7%
S 5475
 
5.3%
L 4908
 
4.8%
M 4086
 
4.0%
Other values (41) 33719
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11921
 
11.6%
A 11033
 
10.7%
E 7851
 
7.6%
R 6058
 
5.9%
O 6013
 
5.8%
I 5990
 
5.8%
N 5827
 
5.7%
S 5475
 
5.3%
L 4908
 
4.8%
M 4086
 
4.0%
Other values (41) 33719
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11921
 
11.6%
A 11033
 
10.7%
E 7851
 
7.6%
R 6058
 
5.9%
O 6013
 
5.8%
I 5990
 
5.8%
N 5827
 
5.7%
S 5475
 
5.3%
L 4908
 
4.8%
M 4086
 
4.0%
Other values (41) 33719
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11921
 
11.6%
A 11033
 
10.7%
E 7851
 
7.6%
R 6058
 
5.9%
O 6013
 
5.8%
I 5990
 
5.8%
N 5827
 
5.7%
S 5475
 
5.3%
L 4908
 
4.8%
M 4086
 
4.0%
Other values (41) 33719
32.8%
Distinct11099
Distinct (%)99.9%
Missing3
Missing (%)< 0.1%
Memory size86.9 KiB
2025-03-12T16:35:57.238585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length31
Mean length14.463276
Min length3

Characters and Unicode

Total characters160687
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11088 ?
Unique (%)99.8%

Sample

1st rowArtur ALEKSANYAN
2nd rowMalkhas AMOYAN
3rd rowSlavik GALSTYAN
4th rowArsen HARUTYUNYAN
5th rowVazgen TEVANYAN
ValueCountFrequency (%)
de 102
 
0.4%
van 97
 
0.4%
maria 76
 
0.3%
laura 47
 
0.2%
mohamed 43
 
0.2%
lee 43
 
0.2%
wang 42
 
0.2%
daniel 42
 
0.2%
anna 41
 
0.2%
kim 39
 
0.2%
Other values (14742) 23287
97.6%
2025-03-12T16:35:57.351267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12751
 
7.9%
A 11037
 
6.9%
a 10023
 
6.2%
E 7838
 
4.9%
i 6506
 
4.0%
e 6129
 
3.8%
R 6054
 
3.8%
O 6019
 
3.7%
I 6004
 
3.7%
N 5828
 
3.6%
Other values (52) 82498
51.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12751
 
7.9%
A 11037
 
6.9%
a 10023
 
6.2%
E 7838
 
4.9%
i 6506
 
4.0%
e 6129
 
3.8%
R 6054
 
3.8%
O 6019
 
3.7%
I 6004
 
3.7%
N 5828
 
3.6%
Other values (52) 82498
51.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12751
 
7.9%
A 11037
 
6.9%
a 10023
 
6.2%
E 7838
 
4.9%
i 6506
 
4.0%
e 6129
 
3.8%
R 6054
 
3.8%
O 6019
 
3.7%
I 6004
 
3.7%
N 5828
 
3.6%
Other values (52) 82498
51.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12751
 
7.9%
A 11037
 
6.9%
a 10023
 
6.2%
E 7838
 
4.9%
i 6506
 
4.0%
e 6129
 
3.8%
R 6054
 
3.8%
O 6019
 
3.7%
I 6004
 
3.7%
N 5828
 
3.6%
Other values (52) 82498
51.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
Male
5658 
Female
5455 

Length

Max length6
Median length4
Mean length4.9817331
Min length4

Characters and Unicode

Total characters55362
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 5658
50.9%
Female 5455
49.1%

Length

2025-03-12T16:35:57.380083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:57.396545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 5658
50.9%
female 5455
49.1%

Most occurring characters

ValueCountFrequency (%)
e 16568
29.9%
a 11113
20.1%
l 11113
20.1%
M 5658
 
10.2%
F 5455
 
9.9%
m 5455
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16568
29.9%
a 11113
20.1%
l 11113
20.1%
M 5658
 
10.2%
F 5455
 
9.9%
m 5455
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16568
29.9%
a 11113
20.1%
l 11113
20.1%
M 5658
 
10.2%
F 5455
 
9.9%
m 5455
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16568
29.9%
a 11113
20.1%
l 11113
20.1%
M 5658
 
10.2%
F 5455
 
9.9%
m 5455
 
9.9%

function
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
Athlete
10924 
Alternate Athlete
 
189

Length

Max length17
Median length7
Mean length7.1700711
Min length7

Characters and Unicode

Total characters79681
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAthlete
2nd rowAthlete
3rd rowAthlete
4th rowAthlete
5th rowAthlete

Common Values

ValueCountFrequency (%)
Athlete 10924
98.3%
Alternate Athlete 189
 
1.7%

Length

2025-03-12T16:35:57.417139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:57.431846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
athlete 11113
98.3%
alternate 189
 
1.7%

Most occurring characters

ValueCountFrequency (%)
t 22604
28.4%
e 22604
28.4%
A 11302
14.2%
l 11302
14.2%
h 11113
13.9%
r 189
 
0.2%
n 189
 
0.2%
a 189
 
0.2%
189
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 22604
28.4%
e 22604
28.4%
A 11302
14.2%
l 11302
14.2%
h 11113
13.9%
r 189
 
0.2%
n 189
 
0.2%
a 189
 
0.2%
189
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 22604
28.4%
e 22604
28.4%
A 11302
14.2%
l 11302
14.2%
h 11113
13.9%
r 189
 
0.2%
n 189
 
0.2%
a 189
 
0.2%
189
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 22604
28.4%
e 22604
28.4%
A 11302
14.2%
l 11302
14.2%
h 11113
13.9%
r 189
 
0.2%
n 189
 
0.2%
a 189
 
0.2%
189
 
0.2%
Distinct206
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:57.540571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33339
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowARM
2nd rowARM
3rd rowARM
4th rowARM
5th rowARM
ValueCountFrequency (%)
usa 619
 
5.6%
fra 601
 
5.4%
aus 475
 
4.3%
ger 457
 
4.1%
jpn 431
 
3.9%
esp 401
 
3.6%
chn 398
 
3.6%
ita 397
 
3.6%
gbr 343
 
3.1%
can 332
 
3.0%
Other values (196) 6659
59.9%
2025-03-12T16:35:57.677608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3967
11.9%
R 3710
 
11.1%
N 2697
 
8.1%
E 2582
 
7.7%
S 2458
 
7.4%
U 2393
 
7.2%
G 1638
 
4.9%
P 1423
 
4.3%
I 1377
 
4.1%
B 1265
 
3.8%
Other values (16) 9829
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3967
11.9%
R 3710
 
11.1%
N 2697
 
8.1%
E 2582
 
7.7%
S 2458
 
7.4%
U 2393
 
7.2%
G 1638
 
4.9%
P 1423
 
4.3%
I 1377
 
4.1%
B 1265
 
3.8%
Other values (16) 9829
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3967
11.9%
R 3710
 
11.1%
N 2697
 
8.1%
E 2582
 
7.7%
S 2458
 
7.4%
U 2393
 
7.2%
G 1638
 
4.9%
P 1423
 
4.3%
I 1377
 
4.1%
B 1265
 
3.8%
Other values (16) 9829
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3967
11.9%
R 3710
 
11.1%
N 2697
 
8.1%
E 2582
 
7.7%
S 2458
 
7.4%
U 2393
 
7.2%
G 1638
 
4.9%
P 1423
 
4.3%
I 1377
 
4.1%
B 1265
 
3.8%
Other values (16) 9829
29.5%
Distinct206
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:57.787604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.6967516
Min length3

Characters and Unicode

Total characters85534
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowArmenia
2nd rowArmenia
3rd rowArmenia
4th rowArmenia
5th rowArmenia
ValueCountFrequency (%)
united 619
 
4.8%
states 619
 
4.8%
france 601
 
4.6%
australia 475
 
3.7%
germany 457
 
3.5%
china 432
 
3.3%
japan 431
 
3.3%
spain 401
 
3.1%
italy 397
 
3.1%
great 343
 
2.6%
Other values (232) 8238
63.3%
2025-03-12T16:35:57.925105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13370
15.6%
n 7426
 
8.7%
e 7205
 
8.4%
i 6878
 
8.0%
r 5702
 
6.7%
t 5167
 
6.0%
l 3381
 
4.0%
d 2648
 
3.1%
o 2451
 
2.9%
s 2189
 
2.6%
Other values (47) 29117
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13370
15.6%
n 7426
 
8.7%
e 7205
 
8.4%
i 6878
 
8.0%
r 5702
 
6.7%
t 5167
 
6.0%
l 3381
 
4.0%
d 2648
 
3.1%
o 2451
 
2.9%
s 2189
 
2.6%
Other values (47) 29117
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13370
15.6%
n 7426
 
8.7%
e 7205
 
8.4%
i 6878
 
8.0%
r 5702
 
6.7%
t 5167
 
6.0%
l 3381
 
4.0%
d 2648
 
3.1%
o 2451
 
2.9%
s 2189
 
2.6%
Other values (47) 29117
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13370
15.6%
n 7426
 
8.7%
e 7205
 
8.4%
i 6878
 
8.0%
r 5702
 
6.7%
t 5167
 
6.0%
l 3381
 
4.0%
d 2648
 
3.1%
o 2451
 
2.9%
s 2189
 
2.6%
Other values (47) 29117
34.0%
Distinct206
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:58.017492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length32
Mean length9.4523531
Min length3

Characters and Unicode

Total characters105044
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowArmenia
2nd rowArmenia
3rd rowArmenia
4th rowArmenia
5th rowArmenia
ValueCountFrequency (%)
of 1265
 
7.9%
republic 716
 
4.5%
united 639
 
4.0%
states 622
 
3.9%
america 619
 
3.9%
france 601
 
3.7%
australia 475
 
3.0%
germany 457
 
2.8%
china 432
 
2.7%
japan 431
 
2.7%
Other values (235) 9789
61.0%
2025-03-12T16:35:58.195401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14143
 
13.5%
e 9501
 
9.0%
i 8265
 
7.9%
n 7471
 
7.1%
r 6375
 
6.1%
t 5238
 
5.0%
4933
 
4.7%
l 4510
 
4.3%
o 4134
 
3.9%
c 2932
 
2.8%
Other values (46) 37542
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14143
 
13.5%
e 9501
 
9.0%
i 8265
 
7.9%
n 7471
 
7.1%
r 6375
 
6.1%
t 5238
 
5.0%
4933
 
4.7%
l 4510
 
4.3%
o 4134
 
3.9%
c 2932
 
2.8%
Other values (46) 37542
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14143
 
13.5%
e 9501
 
9.0%
i 8265
 
7.9%
n 7471
 
7.1%
r 6375
 
6.1%
t 5238
 
5.0%
4933
 
4.7%
l 4510
 
4.3%
o 4134
 
3.9%
c 2932
 
2.8%
Other values (46) 37542
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14143
 
13.5%
e 9501
 
9.0%
i 8265
 
7.9%
n 7471
 
7.1%
r 6375
 
6.1%
t 5238
 
5.0%
4933
 
4.7%
l 4510
 
4.3%
o 4134
 
3.9%
c 2932
 
2.8%
Other values (46) 37542
35.7%
Distinct197
Distinct (%)1.8%
Missing3
Missing (%)< 0.1%
Memory size86.9 KiB
2025-03-12T16:35:58.295204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.7594959
Min length4

Characters and Unicode

Total characters86208
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowArmenia
2nd rowArmenia
3rd rowArmenia
4th rowArmenia
5th rowArmenia
ValueCountFrequency (%)
united 696
 
5.3%
states 696
 
5.3%
france 607
 
4.6%
australia 476
 
3.6%
germany 457
 
3.5%
china 432
 
3.3%
japan 431
 
3.3%
spain 403
 
3.1%
italy 397
 
3.0%
great 357
 
2.7%
Other values (222) 8104
62.1%
2025-03-12T16:35:58.420229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13520
15.7%
n 7554
 
8.8%
e 7382
 
8.6%
i 6928
 
8.0%
r 5718
 
6.6%
t 5398
 
6.3%
l 3388
 
3.9%
d 2734
 
3.2%
o 2352
 
2.7%
s 2293
 
2.7%
Other values (47) 28941
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13520
15.7%
n 7554
 
8.8%
e 7382
 
8.6%
i 6928
 
8.0%
r 5718
 
6.6%
t 5398
 
6.3%
l 3388
 
3.9%
d 2734
 
3.2%
o 2352
 
2.7%
s 2293
 
2.7%
Other values (47) 28941
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13520
15.7%
n 7554
 
8.8%
e 7382
 
8.6%
i 6928
 
8.0%
r 5718
 
6.6%
t 5398
 
6.3%
l 3388
 
3.9%
d 2734
 
3.2%
o 2352
 
2.7%
s 2293
 
2.7%
Other values (47) 28941
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13520
15.7%
n 7554
 
8.8%
e 7382
 
8.6%
i 6928
 
8.0%
r 5718
 
6.6%
t 5398
 
6.3%
l 3388
 
3.9%
d 2734
 
3.2%
o 2352
 
2.7%
s 2293
 
2.7%
Other values (47) 28941
33.6%
Distinct197
Distinct (%)1.8%
Missing3
Missing (%)< 0.1%
Memory size86.9 KiB
2025-03-12T16:35:58.523125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length32
Mean length9.5625563
Min length4

Characters and Unicode

Total characters106240
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowArmenia
2nd rowArmenia
3rd rowArmenia
4th rowArmenia
5th rowArmenia
ValueCountFrequency (%)
of 1357
 
8.4%
republic 733
 
4.5%
united 716
 
4.4%
states 699
 
4.3%
america 696
 
4.3%
france 607
 
3.7%
australia 476
 
2.9%
germany 457
 
2.8%
china 432
 
2.7%
japan 431
 
2.7%
Other values (223) 9602
59.2%
2025-03-12T16:35:58.662247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14352
 
13.5%
e 9628
 
9.1%
i 8382
 
7.9%
n 7603
 
7.2%
r 6470
 
6.1%
t 5466
 
5.1%
5096
 
4.8%
l 4514
 
4.2%
o 4128
 
3.9%
c 2947
 
2.8%
Other values (46) 37654
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14352
 
13.5%
e 9628
 
9.1%
i 8382
 
7.9%
n 7603
 
7.2%
r 6470
 
6.1%
t 5466
 
5.1%
5096
 
4.8%
l 4514
 
4.2%
o 4128
 
3.9%
c 2947
 
2.8%
Other values (46) 37654
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14352
 
13.5%
e 9628
 
9.1%
i 8382
 
7.9%
n 7603
 
7.2%
r 6470
 
6.1%
t 5466
 
5.1%
5096
 
4.8%
l 4514
 
4.2%
o 4128
 
3.9%
c 2947
 
2.8%
Other values (46) 37654
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14352
 
13.5%
e 9628
 
9.1%
i 8382
 
7.9%
n 7603
 
7.2%
r 6470
 
6.1%
t 5466
 
5.1%
5096
 
4.8%
l 4514
 
4.2%
o 4128
 
3.9%
c 2947
 
2.8%
Other values (46) 37654
35.4%
Distinct197
Distinct (%)1.8%
Missing3
Missing (%)< 0.1%
Memory size86.9 KiB
2025-03-12T16:35:58.778374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33330
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowARM
2nd rowARM
3rd rowARM
4th rowARM
5th rowARM
ValueCountFrequency (%)
usa 696
 
6.3%
fra 607
 
5.5%
aus 476
 
4.3%
ger 457
 
4.1%
jpn 431
 
3.9%
esp 403
 
3.6%
chn 398
 
3.6%
ita 397
 
3.6%
gbr 357
 
3.2%
can 336
 
3.0%
Other values (187) 6552
59.0%
2025-03-12T16:35:58.913665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4000
12.0%
R 3677
 
11.0%
N 2675
 
8.0%
S 2558
 
7.7%
E 2550
 
7.7%
U 2423
 
7.3%
G 1645
 
4.9%
P 1373
 
4.1%
I 1355
 
4.1%
B 1286
 
3.9%
Other values (16) 9788
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4000
12.0%
R 3677
 
11.0%
N 2675
 
8.0%
S 2558
 
7.7%
E 2550
 
7.7%
U 2423
 
7.3%
G 1645
 
4.9%
P 1373
 
4.1%
I 1355
 
4.1%
B 1286
 
3.9%
Other values (16) 9788
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4000
12.0%
R 3677
 
11.0%
N 2675
 
8.0%
S 2558
 
7.7%
E 2550
 
7.7%
U 2423
 
7.3%
G 1645
 
4.9%
P 1373
 
4.1%
I 1355
 
4.1%
B 1286
 
3.9%
Other values (16) 9788
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4000
12.0%
R 3677
 
11.0%
N 2675
 
8.0%
S 2558
 
7.7%
E 2550
 
7.7%
U 2423
 
7.3%
G 1645
 
4.9%
P 1373
 
4.1%
I 1355
 
4.1%
B 1286
 
3.9%
Other values (16) 9788
29.4%

height
Real number (ℝ)

High correlation  Zeros 

Distinct74
Distinct (%)0.7%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean81.79883
Minimum0
Maximum222
Zeros6032
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:58.946271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3177
95-th percentile194
Maximum222
Range222
Interquartile range (IQR)177

Descriptive statistics

Standard deviation89.508247
Coefficient of variation (CV)1.0942485
Kurtosis-1.9298165
Mean81.79883
Median Absolute Deviation (MAD)0
Skewness0.19618422
Sum908785
Variance8011.7263
MonotonicityNot monotonic
2025-03-12T16:35:58.977645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6032
54.3%
170 291
 
2.6%
180 246
 
2.2%
175 237
 
2.1%
178 206
 
1.9%
185 189
 
1.7%
183 186
 
1.7%
190 163
 
1.5%
182 156
 
1.4%
168 156
 
1.4%
Other values (64) 3248
29.2%
ValueCountFrequency (%)
0 6032
54.3%
140 1
 
< 0.1%
147 1
 
< 0.1%
148 1
 
< 0.1%
149 2
 
< 0.1%
150 6
 
0.1%
151 1
 
< 0.1%
152 16
 
0.1%
153 7
 
0.1%
154 9
 
0.1%
ValueCountFrequency (%)
222 1
 
< 0.1%
217 1
 
< 0.1%
216 3
 
< 0.1%
215 1
 
< 0.1%
214 1
 
< 0.1%
213 2
 
< 0.1%
212 3
 
< 0.1%
211 12
0.1%
210 7
0.1%
209 2
 
< 0.1%

weight
Real number (ℝ)

High correlation  Zeros 

Distinct55
Distinct (%)0.5%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.2119193
Minimum0
Maximum113
Zeros10792
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:59.009593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum113
Range113
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.109608
Coefficient of variation (CV)5.9268018
Kurtosis34.109539
Mean2.2119193
Median Absolute Deviation (MAD)0
Skewness5.9248041
Sum24570
Variance171.86181
MonotonicityNot monotonic
2025-03-12T16:35:59.040758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10792
97.1%
70 20
 
0.2%
68 14
 
0.1%
65 14
 
0.1%
85 14
 
0.1%
90 12
 
0.1%
95 10
 
0.1%
78 10
 
0.1%
75 9
 
0.1%
63 9
 
0.1%
Other values (45) 204
 
1.8%
ValueCountFrequency (%)
0 10792
97.1%
51 1
 
< 0.1%
52 1
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
56 4
 
< 0.1%
57 2
 
< 0.1%
58 2
 
< 0.1%
59 3
 
< 0.1%
60 9
 
0.1%
ValueCountFrequency (%)
113 1
 
< 0.1%
106 2
 
< 0.1%
105 4
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 6
0.1%
99 2
 
< 0.1%
98 6
0.1%
97 1
 
< 0.1%
96 3
< 0.1%
Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:59.108453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length32
Mean length13.35031
Min length8

Characters and Unicode

Total characters148362
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row['Wrestling']
2nd row['Wrestling']
3rd row['Wrestling']
4th row['Wrestling']
5th row['Wrestling']
ValueCountFrequency (%)
athletics 2020
 
14.7%
swimming 1014
 
7.4%
cycling 584
 
4.2%
football 553
 
4.0%
rowing 493
 
3.6%
hockey 415
 
3.0%
volleyball 407
 
3.0%
handball 386
 
2.8%
judo 378
 
2.7%
basketball 353
 
2.6%
Other values (43) 7166
52.0%
2025-03-12T16:35:59.211889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 22284
15.0%
[ 11113
 
7.5%
] 11113
 
7.5%
i 10228
 
6.9%
l 8616
 
5.8%
t 8329
 
5.6%
n 7948
 
5.4%
e 6677
 
4.5%
o 6030
 
4.1%
a 5695
 
3.8%
Other values (37) 50329
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 22284
15.0%
[ 11113
 
7.5%
] 11113
 
7.5%
i 10228
 
6.9%
l 8616
 
5.8%
t 8329
 
5.6%
n 7948
 
5.4%
e 6677
 
4.5%
o 6030
 
4.1%
a 5695
 
3.8%
Other values (37) 50329
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 22284
15.0%
[ 11113
 
7.5%
] 11113
 
7.5%
i 10228
 
6.9%
l 8616
 
5.8%
t 8329
 
5.6%
n 7948
 
5.4%
e 6677
 
4.5%
o 6030
 
4.1%
a 5695
 
3.8%
Other values (37) 50329
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 22284
15.0%
[ 11113
 
7.5%
] 11113
 
7.5%
i 10228
 
6.9%
l 8616
 
5.8%
t 8329
 
5.6%
n 7948
 
5.4%
e 6677
 
4.5%
o 6030
 
4.1%
a 5695
 
3.8%
Other values (37) 50329
33.9%

events
Text

Distinct643
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
2025-03-12T16:35:59.319083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length225
Median length167
Mean length25.054081
Min length7

Characters and Unicode

Total characters278426
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)1.5%

Sample

1st row["Men's Greco-Roman 97kg"]
2nd row["Men's Greco-Roman 77kg"]
3rd row["Men's Greco-Roman 67kg"]
4th row["Men's Freestyle 57kg"]
5th row["Men's Freestyle 65kg"]
ValueCountFrequency (%)
men's 4774
 
12.8%
women's 4484
 
12.0%
men 2050
 
5.5%
women 1950
 
5.2%
team 1285
 
3.4%
relay 1281
 
3.4%
100m 1138
 
3.0%
x 1049
 
2.8%
4 1049
 
2.8%
individual 1046
 
2.8%
Other values (159) 17323
46.3%
2025-03-12T16:35:59.464658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26316
 
9.5%
e 26241
 
9.4%
' 20522
 
7.4%
" 18514
 
6.6%
n 17764
 
6.4%
s 13527
 
4.9%
m 12820
 
4.6%
[ 11113
 
4.0%
] 11113
 
4.0%
o 10682
 
3.8%
Other values (58) 109814
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 278426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26316
 
9.5%
e 26241
 
9.4%
' 20522
 
7.4%
" 18514
 
6.6%
n 17764
 
6.4%
s 13527
 
4.9%
m 12820
 
4.6%
[ 11113
 
4.0%
] 11113
 
4.0%
o 10682
 
3.8%
Other values (58) 109814
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 278426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26316
 
9.5%
e 26241
 
9.4%
' 20522
 
7.4%
" 18514
 
6.6%
n 17764
 
6.4%
s 13527
 
4.9%
m 12820
 
4.6%
[ 11113
 
4.0%
] 11113
 
4.0%
o 10682
 
3.8%
Other values (58) 109814
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 278426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26316
 
9.5%
e 26241
 
9.4%
' 20522
 
7.4%
" 18514
 
6.6%
n 17764
 
6.4%
s 13527
 
4.9%
m 12820
 
4.6%
[ 11113
 
4.0%
] 11113
 
4.0%
o 10682
 
3.8%
Other values (58) 109814
39.4%
Distinct5518
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size86.9 KiB
Minimum1954-12-01 00:00:00
Maximum2012-08-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T16:35:59.496065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:59.531127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

birth_place
Text

Missing 

Distinct4725
Distinct (%)54.1%
Missing2386
Missing (%)21.5%
Memory size86.9 KiB
2025-03-12T16:35:59.642441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length33
Mean length9.1250143
Min length2

Characters and Unicode

Total characters79634
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3488 ?
Unique (%)40.0%

Sample

1st rowGYUMRI
2nd rowYEREVAN
3rd rowMASIS
4th rowPOKR VEDI
5th rowPEREIRA
ValueCountFrequency (%)
ca 136
 
1.1%
de 126
 
1.1%
nsw 118
 
1.0%
on 118
 
1.0%
vic 88
 
0.7%
san 82
 
0.7%
city 78
 
0.7%
qld 75
 
0.6%
budapest 66
 
0.6%
la 61
 
0.5%
Other values (5001) 11002
92.1%
2025-03-12T16:35:59.784483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 9623
 
12.1%
E 6401
 
8.0%
N 6344
 
8.0%
O 5713
 
7.2%
I 5018
 
6.3%
R 4875
 
6.1%
S 4288
 
5.4%
L 4022
 
5.1%
T 3630
 
4.6%
3223
 
4.0%
Other values (26) 26497
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 9623
 
12.1%
E 6401
 
8.0%
N 6344
 
8.0%
O 5713
 
7.2%
I 5018
 
6.3%
R 4875
 
6.1%
S 4288
 
5.4%
L 4022
 
5.1%
T 3630
 
4.6%
3223
 
4.0%
Other values (26) 26497
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 9623
 
12.1%
E 6401
 
8.0%
N 6344
 
8.0%
O 5713
 
7.2%
I 5018
 
6.3%
R 4875
 
6.1%
S 4288
 
5.4%
L 4022
 
5.1%
T 3630
 
4.6%
3223
 
4.0%
Other values (26) 26497
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 9623
 
12.1%
E 6401
 
8.0%
N 6344
 
8.0%
O 5713
 
7.2%
I 5018
 
6.3%
R 4875
 
6.1%
S 4288
 
5.4%
L 4022
 
5.1%
T 3630
 
4.6%
3223
 
4.0%
Other values (26) 26497
33.3%

birth_country
Text

Missing 

Distinct205
Distinct (%)2.2%
Missing1638
Missing (%)14.7%
Memory size86.9 KiB
2025-03-12T16:35:59.878088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.9003694
Min length4

Characters and Unicode

Total characters74856
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st rowArmenia
2nd rowArmenia
3rd rowArmenia
4th rowArmenia
5th rowColombia
ValueCountFrequency (%)
united 705
 
6.2%
states 705
 
6.2%
france 552
 
4.9%
china 397
 
3.5%
germany 396
 
3.5%
japan 393
 
3.5%
australia 391
 
3.4%
italy 353
 
3.1%
spain 341
 
3.0%
great 338
 
3.0%
Other values (231) 6783
59.7%
2025-03-12T16:36:00.001324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11573
15.5%
n 6642
 
8.9%
e 6569
 
8.8%
i 6091
 
8.1%
t 4974
 
6.6%
r 4854
 
6.5%
l 2785
 
3.7%
d 2412
 
3.2%
s 2094
 
2.8%
o 2032
 
2.7%
Other values (49) 24830
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11573
15.5%
n 6642
 
8.9%
e 6569
 
8.8%
i 6091
 
8.1%
t 4974
 
6.6%
r 4854
 
6.5%
l 2785
 
3.7%
d 2412
 
3.2%
s 2094
 
2.8%
o 2032
 
2.7%
Other values (49) 24830
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11573
15.5%
n 6642
 
8.9%
e 6569
 
8.8%
i 6091
 
8.1%
t 4974
 
6.6%
r 4854
 
6.5%
l 2785
 
3.7%
d 2412
 
3.2%
s 2094
 
2.8%
o 2032
 
2.7%
Other values (49) 24830
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11573
15.5%
n 6642
 
8.9%
e 6569
 
8.8%
i 6091
 
8.1%
t 4974
 
6.6%
r 4854
 
6.5%
l 2785
 
3.7%
d 2412
 
3.2%
s 2094
 
2.8%
o 2032
 
2.7%
Other values (49) 24830
33.2%

residence_place
Text

Missing 

Distinct2752
Distinct (%)40.4%
Missing4309
Missing (%)38.8%
Memory size86.9 KiB
2025-03-12T16:36:00.121929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length30
Mean length8.9594356
Min length2

Characters and Unicode

Total characters60960
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1957 ?
Unique (%)28.8%

Sample

1st rowGYUMRI
2nd rowYEREVAN
3rd rowYEREVAN
4th rowYEREVAN
5th rowMELBOURNE
ValueCountFrequency (%)
ca 140
 
1.5%
fl 122
 
1.3%
tx 94
 
1.0%
paris 93
 
1.0%
beijing 87
 
0.9%
nsw 84
 
0.9%
qld 81
 
0.9%
on 76
 
0.8%
san 70
 
0.8%
madrid 64
 
0.7%
Other values (2992) 8359
90.2%
2025-03-12T16:36:00.277182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 7019
 
11.5%
E 4931
 
8.1%
N 4732
 
7.8%
O 4278
 
7.0%
I 3774
 
6.2%
R 3659
 
6.0%
L 3438
 
5.6%
S 3289
 
5.4%
T 2870
 
4.7%
2466
 
4.0%
Other values (28) 20504
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 7019
 
11.5%
E 4931
 
8.1%
N 4732
 
7.8%
O 4278
 
7.0%
I 3774
 
6.2%
R 3659
 
6.0%
L 3438
 
5.6%
S 3289
 
5.4%
T 2870
 
4.7%
2466
 
4.0%
Other values (28) 20504
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 7019
 
11.5%
E 4931
 
8.1%
N 4732
 
7.8%
O 4278
 
7.0%
I 3774
 
6.2%
R 3659
 
6.0%
L 3438
 
5.6%
S 3289
 
5.4%
T 2870
 
4.7%
2466
 
4.0%
Other values (28) 20504
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 7019
 
11.5%
E 4931
 
8.1%
N 4732
 
7.8%
O 4278
 
7.0%
I 3774
 
6.2%
R 3659
 
6.0%
L 3438
 
5.6%
S 3289
 
5.4%
T 2870
 
4.7%
2466
 
4.0%
Other values (28) 20504
33.6%

residence_country
Text

Missing 

Distinct177
Distinct (%)2.1%
Missing2825
Missing (%)25.4%
Memory size86.9 KiB
2025-03-12T16:36:00.366170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length8.0878378
Min length4

Characters and Unicode

Total characters67032
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st rowArmenia
2nd rowArmenia
3rd rowArmenia
4th rowArmenia
5th rowArmenia
ValueCountFrequency (%)
united 979
 
9.7%
states 979
 
9.7%
france 520
 
5.1%
germany 437
 
4.3%
spain 383
 
3.8%
australia 354
 
3.5%
great 348
 
3.4%
britain 348
 
3.4%
china 340
 
3.4%
italy 314
 
3.1%
Other values (197) 5138
50.7%
2025-03-12T16:36:00.485799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9975
14.9%
e 6107
 
9.1%
n 5958
 
8.9%
t 5513
 
8.2%
i 5305
 
7.9%
r 4274
 
6.4%
d 2355
 
3.5%
l 2337
 
3.5%
s 2076
 
3.1%
1852
 
2.8%
Other values (46) 21280
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9975
14.9%
e 6107
 
9.1%
n 5958
 
8.9%
t 5513
 
8.2%
i 5305
 
7.9%
r 4274
 
6.4%
d 2355
 
3.5%
l 2337
 
3.5%
s 2076
 
3.1%
1852
 
2.8%
Other values (46) 21280
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9975
14.9%
e 6107
 
9.1%
n 5958
 
8.9%
t 5513
 
8.2%
i 5305
 
7.9%
r 4274
 
6.4%
d 2355
 
3.5%
l 2337
 
3.5%
s 2076
 
3.1%
1852
 
2.8%
Other values (46) 21280
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9975
14.9%
e 6107
 
9.1%
n 5958
 
8.9%
t 5513
 
8.2%
i 5305
 
7.9%
r 4274
 
6.4%
d 2355
 
3.5%
l 2337
 
3.5%
s 2076
 
3.1%
1852
 
2.8%
Other values (46) 21280
31.7%

nickname
Text

Missing 

Distinct2623
Distinct (%)88.4%
Missing8147
Missing (%)73.3%
Memory size86.9 KiB
2025-03-12T16:36:00.595474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length369
Median length305
Mean length15.813554
Min length1

Characters and Unicode

Total characters46903
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2422 ?
Unique (%)81.7%

Sample

1st rowWhite Bear
2nd rowBad Boy
3rd rowEarlier in her career she was nicknamed the 'Poor Olympian' due to the financial challenges she faced
4th rowDiamante Negro (Black Diamond), Martillo Xalapeno (Hammer from Xalapa, given to him by Spanish triathlete Javier Gomez Noya)
5th rowNutria ("Otter" in Spanish)
ValueCountFrequency (%)
the 348
 
4.1%
of 113
 
1.3%
to 99
 
1.2%
a 89
 
1.1%
his 82
 
1.0%
in 78
 
0.9%
her 76
 
0.9%
and 68
 
0.8%
because 64
 
0.8%
little 56
 
0.7%
Other values (4192) 7328
87.2%
2025-03-12T16:36:00.746019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5443
 
11.6%
e 3991
 
8.5%
a 3826
 
8.2%
i 3026
 
6.5%
o 2587
 
5.5%
n 2442
 
5.2%
t 2084
 
4.4%
r 2009
 
4.3%
s 1684
 
3.6%
l 1640
 
3.5%
Other values (86) 18171
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5443
 
11.6%
e 3991
 
8.5%
a 3826
 
8.2%
i 3026
 
6.5%
o 2587
 
5.5%
n 2442
 
5.2%
t 2084
 
4.4%
r 2009
 
4.3%
s 1684
 
3.6%
l 1640
 
3.5%
Other values (86) 18171
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5443
 
11.6%
e 3991
 
8.5%
a 3826
 
8.2%
i 3026
 
6.5%
o 2587
 
5.5%
n 2442
 
5.2%
t 2084
 
4.4%
r 2009
 
4.3%
s 1684
 
3.6%
l 1640
 
3.5%
Other values (86) 18171
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5443
 
11.6%
e 3991
 
8.5%
a 3826
 
8.2%
i 3026
 
6.5%
o 2587
 
5.5%
n 2442
 
5.2%
t 2084
 
4.4%
r 2009
 
4.3%
s 1684
 
3.6%
l 1640
 
3.5%
Other values (86) 18171
38.7%

hobbies
Text

Missing 

Distinct3405
Distinct (%)80.9%
Missing6906
Missing (%)62.1%
Memory size86.9 KiB
2025-03-12T16:36:00.915395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length540
Median length284
Mean length46.12812
Min length3

Characters and Unicode

Total characters194061
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3241 ?
Unique (%)77.0%

Sample

1st rowPlaying and watching football
2nd rowMusic
3rd rowRunning, watching movies, football, chatting with friends
4th rowMusic, films
5th rowSpending time with friends, playing the piano, going to the cinema
ValueCountFrequency (%)
and 1102
 
3.9%
to 838
 
3.0%
with 812
 
2.9%
playing 771
 
2.7%
reading 703
 
2.5%
the 670
 
2.4%
watching 662
 
2.3%
time 651
 
2.3%
music 626
 
2.2%
spending 616
 
2.2%
Other values (3268) 20826
73.6%
2025-03-12T16:36:01.064025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24075
 
12.4%
i 18262
 
9.4%
n 15906
 
8.2%
e 12591
 
6.5%
a 11942
 
6.2%
g 10552
 
5.4%
t 10219
 
5.3%
o 10058
 
5.2%
s 9160
 
4.7%
l 7074
 
3.6%
Other values (76) 64222
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194061
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24075
 
12.4%
i 18262
 
9.4%
n 15906
 
8.2%
e 12591
 
6.5%
a 11942
 
6.2%
g 10552
 
5.4%
t 10219
 
5.3%
o 10058
 
5.2%
s 9160
 
4.7%
l 7074
 
3.6%
Other values (76) 64222
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194061
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24075
 
12.4%
i 18262
 
9.4%
n 15906
 
8.2%
e 12591
 
6.5%
a 11942
 
6.2%
g 10552
 
5.4%
t 10219
 
5.3%
o 10058
 
5.2%
s 9160
 
4.7%
l 7074
 
3.6%
Other values (76) 64222
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194061
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24075
 
12.4%
i 18262
 
9.4%
n 15906
 
8.2%
e 12591
 
6.5%
a 11942
 
6.2%
g 10552
 
5.4%
t 10219
 
5.3%
o 10058
 
5.2%
s 9160
 
4.7%
l 7074
 
3.6%
Other values (76) 64222
33.1%

occupation
Text

Missing 

Distinct927
Distinct (%)9.7%
Missing1529
Missing (%)13.8%
Memory size86.9 KiB
2025-03-12T16:36:01.184947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length290
Median length7
Mean length12.421432
Min length4

Characters and Unicode

Total characters119047
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique783 ?
Unique (%)8.2%

Sample

1st rowAthlete
2nd rowAthlete
3rd rowAthlete
4th rowAthlete
5th rowAthlete
ValueCountFrequency (%)
athlete 9102
55.6%
student 1297
 
7.9%
coach 338
 
2.1%
trainer 197
 
1.2%
horse 182
 
1.1%
soldier 170
 
1.0%
the 161
 
1.0%
business 153
 
0.9%
in 148
 
0.9%
police 147
 
0.9%
Other values (1330) 4472
27.3%
2025-03-12T16:36:01.342361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 23733
19.9%
t 23129
19.4%
h 10341
8.7%
l 10297
8.6%
A 9194
 
7.7%
6783
 
5.7%
n 3685
 
3.1%
r 3629
 
3.0%
s 3594
 
3.0%
a 2993
 
2.5%
Other values (69) 21669
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 23733
19.9%
t 23129
19.4%
h 10341
8.7%
l 10297
8.6%
A 9194
 
7.7%
6783
 
5.7%
n 3685
 
3.1%
r 3629
 
3.0%
s 3594
 
3.0%
a 2993
 
2.5%
Other values (69) 21669
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 23733
19.9%
t 23129
19.4%
h 10341
8.7%
l 10297
8.6%
A 9194
 
7.7%
6783
 
5.7%
n 3685
 
3.1%
r 3629
 
3.0%
s 3594
 
3.0%
a 2993
 
2.5%
Other values (69) 21669
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 23733
19.9%
t 23129
19.4%
h 10341
8.7%
l 10297
8.6%
A 9194
 
7.7%
6783
 
5.7%
n 3685
 
3.1%
r 3629
 
3.0%
s 3594
 
3.0%
a 2993
 
2.5%
Other values (69) 21669
18.2%

education
Text

Missing 

Distinct5352
Distinct (%)96.6%
Missing5575
Missing (%)50.2%
Memory size86.9 KiB
2025-03-12T16:36:01.446947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length384
Median length264
Mean length83.559227
Min length6

Characters and Unicode

Total characters462751
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5226 ?
Unique (%)94.4%

Sample

1st rowGraduated from Shirak State University (Gyumri, ARM)
2nd rowGraduated with a Master's degree from the Armenian State Institute of Physical Culture and Sport (2023)
3rd rowStudied at the Armenian State Institute of Physical Culture and Sport (Yerevan, ARM)
4th rowPhysical Education at Jaime Isaza Cadavid Colombian Polytechnic, Medellin (COL)
5th rowStudied Medicine at the University of Guadalajara (MEX)
ValueCountFrequency (%)
university 4829
 
7.2%
at 3413
 
5.1%
in 3372
 
5.0%
of 3162
 
4.7%
a 1905
 
2.8%
degree 1734
 
2.6%
the 1648
 
2.5%
and 1648
 
2.5%
from 1523
 
2.3%
usa 1429
 
2.1%
Other values (4773) 42202
63.1%
2025-03-12T16:36:01.590819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61347
 
13.3%
e 36697
 
7.9%
i 34785
 
7.5%
a 28744
 
6.2%
t 28563
 
6.2%
n 28098
 
6.1%
r 22333
 
4.8%
o 21087
 
4.6%
s 18593
 
4.0%
d 14767
 
3.2%
Other values (97) 167737
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 462751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
61347
 
13.3%
e 36697
 
7.9%
i 34785
 
7.5%
a 28744
 
6.2%
t 28563
 
6.2%
n 28098
 
6.1%
r 22333
 
4.8%
o 21087
 
4.6%
s 18593
 
4.0%
d 14767
 
3.2%
Other values (97) 167737
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 462751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
61347
 
13.3%
e 36697
 
7.9%
i 34785
 
7.5%
a 28744
 
6.2%
t 28563
 
6.2%
n 28098
 
6.1%
r 22333
 
4.8%
o 21087
 
4.6%
s 18593
 
4.0%
d 14767
 
3.2%
Other values (97) 167737
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 462751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
61347
 
13.3%
e 36697
 
7.9%
i 34785
 
7.5%
a 28744
 
6.2%
t 28563
 
6.2%
n 28098
 
6.1%
r 22333
 
4.8%
o 21087
 
4.6%
s 18593
 
4.0%
d 14767
 
3.2%
Other values (97) 167737
36.2%

family
Text

Missing 

Distinct5447
Distinct (%)98.0%
Missing5552
Missing (%)50.0%
Memory size86.9 KiB
2025-03-12T16:36:01.711318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length322
Median length179
Mean length52.189354
Min length4

Characters and Unicode

Total characters290225
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5380 ?
Unique (%)96.7%

Sample

1st rowFather, Gevorg Aleksanyan
2nd rowWife, Diana (married October 2022). Daughter, Marias (born 2023)
3rd rowWife, Sona (married November 2023)
4th rowFather, Jose Otoniel. Mother, Maria Rudy. Has three siblings
5th rowOne daughter, Lana-Rose, who lives in France
ValueCountFrequency (%)
father 3251
 
8.0%
mother 3057
 
7.6%
brother 1480
 
3.7%
sister 1329
 
3.3%
and 954
 
2.4%
wife 732
 
1.8%
older 709
 
1.8%
partner 658
 
1.6%
born 567
 
1.4%
younger 507
 
1.3%
Other values (9526) 27150
67.2%
2025-03-12T16:36:01.868699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34852
 
12.0%
e 26928
 
9.3%
r 24588
 
8.5%
a 21582
 
7.4%
t 15938
 
5.5%
n 14205
 
4.9%
o 14098
 
4.9%
i 14078
 
4.9%
, 12976
 
4.5%
h 12525
 
4.3%
Other values (93) 98455
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 290225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34852
 
12.0%
e 26928
 
9.3%
r 24588
 
8.5%
a 21582
 
7.4%
t 15938
 
5.5%
n 14205
 
4.9%
o 14098
 
4.9%
i 14078
 
4.9%
, 12976
 
4.5%
h 12525
 
4.3%
Other values (93) 98455
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 290225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34852
 
12.0%
e 26928
 
9.3%
r 24588
 
8.5%
a 21582
 
7.4%
t 15938
 
5.5%
n 14205
 
4.9%
o 14098
 
4.9%
i 14078
 
4.9%
, 12976
 
4.5%
h 12525
 
4.3%
Other values (93) 98455
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 290225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34852
 
12.0%
e 26928
 
9.3%
r 24588
 
8.5%
a 21582
 
7.4%
t 15938
 
5.5%
n 14205
 
4.9%
o 14098
 
4.9%
i 14078
 
4.9%
, 12976
 
4.5%
h 12525
 
4.3%
Other values (93) 98455
33.9%

lang
Text

Missing 

Distinct738
Distinct (%)7.0%
Missing508
Missing (%)4.6%
Memory size86.9 KiB
2025-03-12T16:36:01.946644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length56
Mean length11.447148
Min length1

Characters and Unicode

Total characters121397
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique449 ?
Unique (%)4.2%

Sample

1st rowArmenian, English, Russian
2nd rowArmenian
3rd rowArmenian
4th rowArmenian
5th rowArmenian, Russian
ValueCountFrequency (%)
english 6176
38.8%
spanish 1373
 
8.6%
french 1243
 
7.8%
german 820
 
5.2%
italian 519
 
3.3%
mandarin 494
 
3.1%
japanese 441
 
2.8%
portuguese 416
 
2.6%
russian 409
 
2.6%
dutch 402
 
2.5%
Other values (201) 3613
22.7%
2025-03-12T16:36:02.062302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 14797
12.2%
i 12194
 
10.0%
h 10283
 
8.5%
s 10215
 
8.4%
a 8953
 
7.4%
l 7375
 
6.1%
g 7117
 
5.9%
E 6205
 
5.1%
e 5471
 
4.5%
5301
 
4.4%
Other values (48) 33486
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 14797
12.2%
i 12194
 
10.0%
h 10283
 
8.5%
s 10215
 
8.4%
a 8953
 
7.4%
l 7375
 
6.1%
g 7117
 
5.9%
E 6205
 
5.1%
e 5471
 
4.5%
5301
 
4.4%
Other values (48) 33486
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 14797
12.2%
i 12194
 
10.0%
h 10283
 
8.5%
s 10215
 
8.4%
a 8953
 
7.4%
l 7375
 
6.1%
g 7117
 
5.9%
E 6205
 
5.1%
e 5471
 
4.5%
5301
 
4.4%
Other values (48) 33486
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 14797
12.2%
i 12194
 
10.0%
h 10283
 
8.5%
s 10215
 
8.4%
a 8953
 
7.4%
l 7375
 
6.1%
g 7117
 
5.9%
E 6205
 
5.1%
e 5471
 
4.5%
5301
 
4.4%
Other values (48) 33486
27.6%

coach
Text

Missing 

Distinct5651
Distinct (%)68.7%
Missing2891
Missing (%)26.0%
Memory size86.9 KiB
2025-03-12T16:36:02.160149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length349
Median length229
Mean length40.863415
Min length5

Characters and Unicode

Total characters335979
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4807 ?
Unique (%)58.5%

Sample

1st rowGevorg Aleksanyan (ARM), father
2nd rowPersonal: Martin Alekhanyan (ARM).<br>National: Armen Babalaryan (ARM)
3rd rowNational: Habetnak Kurghinyan
4th rowNational: Habetnak Kurghinyan (ARM)
5th rowPersonal: Brent Vallance (AUS)
ValueCountFrequency (%)
national 3974
 
8.9%
personal 2580
 
5.8%
club 1014
 
2.3%
usa 694
 
1.6%
fra 493
 
1.1%
esp 469
 
1.1%
ita 456
 
1.0%
gbr 416
 
0.9%
aus 410
 
0.9%
ger 399
 
0.9%
Other values (8746) 33536
75.5%
2025-03-12T16:36:02.295869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36369
 
10.8%
a 28951
 
8.6%
n 18966
 
5.6%
o 18245
 
5.4%
e 17632
 
5.2%
l 17239
 
5.1%
i 17178
 
5.1%
r 15285
 
4.5%
t 10540
 
3.1%
) 10049
 
3.0%
Other values (98) 145525
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 335979
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36369
 
10.8%
a 28951
 
8.6%
n 18966
 
5.6%
o 18245
 
5.4%
e 17632
 
5.2%
l 17239
 
5.1%
i 17178
 
5.1%
r 15285
 
4.5%
t 10540
 
3.1%
) 10049
 
3.0%
Other values (98) 145525
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 335979
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36369
 
10.8%
a 28951
 
8.6%
n 18966
 
5.6%
o 18245
 
5.4%
e 17632
 
5.2%
l 17239
 
5.1%
i 17178
 
5.1%
r 15285
 
4.5%
t 10540
 
3.1%
) 10049
 
3.0%
Other values (98) 145525
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 335979
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36369
 
10.8%
a 28951
 
8.6%
n 18966
 
5.6%
o 18245
 
5.4%
e 17632
 
5.2%
l 17239
 
5.1%
i 17178
 
5.1%
r 15285
 
4.5%
t 10540
 
3.1%
) 10049
 
3.0%
Other values (98) 145525
43.3%

reason
Text

Missing 

Distinct5734
Distinct (%)98.1%
Missing5267
Missing (%)47.4%
Memory size86.9 KiB
2025-03-12T16:36:02.413300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length972
Median length582
Mean length188.9362
Min length10

Characters and Unicode

Total characters1104521
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5673 ?
Unique (%)97.0%

Sample

1st rowHe followed his father and his uncle into the sport
2nd rowWhile doing karate he noticed wrestlers training and decided to give it a try. He also tried judo but his father, a former wrestler, did not allow him to do both, so he chose wrestling. (sport.mediamax.am, 10 July 2017)
3rd row“My family did not like wrestling very much. At first I wanted to do boxing but my older friends advised me to go to wrestling training, and after a week, I started to like the sport.” (myInfo)
4th row"I was thrown over [an argument about] a Pokemon card and wanted to learn to throw immediately." (Athlete, 25 Jun 2024)
5th row“I started running when I was in primary school, like we have junior championships. It's where I started running and it’s where I started to notice that, if I work hard I will be a great athlete.” (olympics.com, 24 Apr 2024)
ValueCountFrequency (%)
the 7488
 
3.8%
to 7222
 
3.6%
i 6225
 
3.1%
and 6063
 
3.1%
a 5332
 
2.7%
was 4732
 
2.4%
in 4089
 
2.1%
her 3578
 
1.8%
his 2612
 
1.3%
she 2417
 
1.2%
Other values (10902) 148635
74.9%
2025-03-12T16:36:02.565921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192598
17.4%
e 95648
 
8.7%
t 78260
 
7.1%
a 69853
 
6.3%
o 64792
 
5.9%
i 58234
 
5.3%
n 57875
 
5.2%
s 50759
 
4.6%
r 50467
 
4.6%
h 45414
 
4.1%
Other values (98) 340621
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1104521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
192598
17.4%
e 95648
 
8.7%
t 78260
 
7.1%
a 69853
 
6.3%
o 64792
 
5.9%
i 58234
 
5.3%
n 57875
 
5.2%
s 50759
 
4.6%
r 50467
 
4.6%
h 45414
 
4.1%
Other values (98) 340621
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1104521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
192598
17.4%
e 95648
 
8.7%
t 78260
 
7.1%
a 69853
 
6.3%
o 64792
 
5.9%
i 58234
 
5.3%
n 57875
 
5.2%
s 50759
 
4.6%
r 50467
 
4.6%
h 45414
 
4.1%
Other values (98) 340621
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1104521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
192598
17.4%
e 95648
 
8.7%
t 78260
 
7.1%
a 69853
 
6.3%
o 64792
 
5.9%
i 58234
 
5.3%
n 57875
 
5.2%
s 50759
 
4.6%
r 50467
 
4.6%
h 45414
 
4.1%
Other values (98) 340621
30.8%

hero
Text

Missing 

Distinct2456
Distinct (%)74.1%
Missing7798
Missing (%)70.2%
Memory size86.9 KiB
2025-03-12T16:36:02.673501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length984
Median length626
Mean length242.18069
Min length7

Characters and Unicode

Total characters802829
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2172 ?
Unique (%)65.5%

Sample

1st rowFootballer Zinedine Zidane (FRA), World Cup winner (1998) and European champion (2000) with France, won the Champions League as a player and three times as a manager with Real Madrid, three-time FIFA World Player of the Year
2nd rowWrestler Armen Nazaryan (ARM, BUL), two-time Olympic champion (1996, 2000) and 2004 bronze medallist. Eight-time world championship medallist (three gold, two silver, three bronze)
3rd rowRace walker Luis Fernando Lopez (COL), four-time Olympian (2004, 2008, 2012, 2016), 2011 world champion (20km walk)
4th rowBoxer Muhammad Ali, born Cassius Clay (USA), former undisputed heavyweight champion of the world, 1960 Olympic champion (light heavyweight), nicknamed 'The Greatest' and regarded as one of the most significant sports figures of the 20th century
5th rowSprinter Shelly-Ann Fraser-Pryce (JAM), three time Olympic champion (four silver, one bronze), 16-time world championship medallist (10 gold, five silver, one bronze). In the 100m, two-time Olympic champion (2008, 2012), five-time world champion (2009, 2013, 2015, 2019, 2022). </p><p>"Shelly-Ann, she is consistent. She is a mother but she still loves what she is doing and she is still performing as she did before." (Tales of Hagie Drammeh Youtube, 16 Jan 2023)</p><p>Sprinter Marie-Josee Ta Lou (CIV), three fourth places at the Olympic Games (2016, 2020), double world silver medallist (100m-200m) in 2017, world bronze medallist in 2019 (100m)
ValueCountFrequency (%)
medallist 4415
 
3.9%
olympic 3499
 
3.1%
world 3457
 
3.1%
silver 3170
 
2.8%
gold 3065
 
2.7%
champion 2680
 
2.4%
bronze 2613
 
2.3%
one 2075
 
1.9%
championship 1882
 
1.7%
two 1786
 
1.6%
Other values (7611) 83138
74.4%
2025-03-12T16:36:02.816083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108628
 
13.5%
e 59125
 
7.4%
i 49911
 
6.2%
l 39911
 
5.0%
o 38969
 
4.9%
a 38372
 
4.8%
t 35045
 
4.4%
r 33949
 
4.2%
n 33638
 
4.2%
m 29063
 
3.6%
Other values (87) 336218
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 802829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
108628
 
13.5%
e 59125
 
7.4%
i 49911
 
6.2%
l 39911
 
5.0%
o 38969
 
4.9%
a 38372
 
4.8%
t 35045
 
4.4%
r 33949
 
4.2%
n 33638
 
4.2%
m 29063
 
3.6%
Other values (87) 336218
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 802829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
108628
 
13.5%
e 59125
 
7.4%
i 49911
 
6.2%
l 39911
 
5.0%
o 38969
 
4.9%
a 38372
 
4.8%
t 35045
 
4.4%
r 33949
 
4.2%
n 33638
 
4.2%
m 29063
 
3.6%
Other values (87) 336218
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 802829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
108628
 
13.5%
e 59125
 
7.4%
i 49911
 
6.2%
l 39911
 
5.0%
o 38969
 
4.9%
a 38372
 
4.8%
t 35045
 
4.4%
r 33949
 
4.2%
n 33638
 
4.2%
m 29063
 
3.6%
Other values (87) 336218
41.9%

influence
Text

Missing 

Distinct1529
Distinct (%)71.0%
Missing8958
Missing (%)80.6%
Memory size86.9 KiB
2025-03-12T16:36:02.932290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length756
Median length517
Mean length81.467285
Min length5

Characters and Unicode

Total characters175562
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1497 ?
Unique (%)69.5%

Sample

1st rowHis father, Gevorg Aleksanyan
2nd row"My coach Luke Preston. We've been a team for the last 12 years." (Athlete, 25 Jun 2024)
3rd rowMomodou Lamin Kujabi, a former international athlete from The Gambia who was once his physical education teacher
4th rowHer parents
5th rowFictional boxer Rocky Balboa from the "Rocky" movie series. "I saw the entire Rocky saga with my father, and I said, 'Wow. I want to do what he does, I want to be just as good’. I even told my parents that I wanted to be a boxer... Although I didn't go into boxing, the truth is that Rocky Balboa taught me to be someone, and I always think that if he went from street fights to being world champion, I could do something like that too." (heraldodemexico.com.mx, 2 Aug 2021)
ValueCountFrequency (%)
and 1220
 
4.0%
her 1089
 
3.5%
his 927
 
3.0%
my 669
 
2.2%
me 668
 
2.2%
the 658
 
2.1%
to 646
 
2.1%
i 608
 
2.0%
coach 584
 
1.9%
in 415
 
1.4%
Other values (4814) 23250
75.6%
2025-03-12T16:36:03.088938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28604
16.3%
e 16185
 
9.2%
a 11717
 
6.7%
t 9863
 
5.6%
r 8831
 
5.0%
o 8804
 
5.0%
i 8440
 
4.8%
n 8359
 
4.8%
s 7694
 
4.4%
h 7670
 
4.4%
Other values (86) 59395
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 175562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28604
16.3%
e 16185
 
9.2%
a 11717
 
6.7%
t 9863
 
5.6%
r 8831
 
5.0%
o 8804
 
5.0%
i 8440
 
4.8%
n 8359
 
4.8%
s 7694
 
4.4%
h 7670
 
4.4%
Other values (86) 59395
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 175562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28604
16.3%
e 16185
 
9.2%
a 11717
 
6.7%
t 9863
 
5.6%
r 8831
 
5.0%
o 8804
 
5.0%
i 8440
 
4.8%
n 8359
 
4.8%
s 7694
 
4.4%
h 7670
 
4.4%
Other values (86) 59395
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 175562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28604
16.3%
e 16185
 
9.2%
a 11717
 
6.7%
t 9863
 
5.6%
r 8831
 
5.0%
o 8804
 
5.0%
i 8440
 
4.8%
n 8359
 
4.8%
s 7694
 
4.4%
h 7670
 
4.4%
Other values (86) 59395
33.8%

philosophy
Text

Missing 

Distinct2761
Distinct (%)99.2%
Missing8330
Missing (%)75.0%
Memory size86.9 KiB
2025-03-12T16:36:03.201085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length954
Median length410
Mean length119.84585
Min length1

Characters and Unicode

Total characters333531
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2749 ?
Unique (%)98.8%

Sample

1st row"Wrestling is my life." (mediamax.am. 18 May 2016)
2nd row"To become a good athlete, you first have to be a good person." (ankakh.com, 6 Oct 2018)
3rd row“Nothing is impossible, set goals in front of you, fight and achieve it.” (Instagram, 13 May 2023)
4th row"If you believe in yourself, never be discouraged." (worldathletics.org, 17 Dec 2019)
5th row"What does not kill you makes you stronger." (Athlete, 7 Jul 2024)
ValueCountFrequency (%)
the 2043
 
3.5%
to 1910
 
3.3%
you 1638
 
2.8%
and 1537
 
2.6%
i 1316
 
2.3%
is 1140
 
2.0%
a 977
 
1.7%
it 799
 
1.4%
in 756
 
1.3%
2024 665
 
1.1%
Other values (5252) 45441
78.0%
2025-03-12T16:36:03.350825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55462
16.6%
e 27715
 
8.3%
t 22126
 
6.6%
o 20614
 
6.2%
a 17924
 
5.4%
n 16475
 
4.9%
i 15036
 
4.5%
s 13399
 
4.0%
r 13191
 
4.0%
h 10641
 
3.2%
Other values (87) 120948
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
55462
16.6%
e 27715
 
8.3%
t 22126
 
6.6%
o 20614
 
6.2%
a 17924
 
5.4%
n 16475
 
4.9%
i 15036
 
4.5%
s 13399
 
4.0%
r 13191
 
4.0%
h 10641
 
3.2%
Other values (87) 120948
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
55462
16.6%
e 27715
 
8.3%
t 22126
 
6.6%
o 20614
 
6.2%
a 17924
 
5.4%
n 16475
 
4.9%
i 15036
 
4.5%
s 13399
 
4.0%
r 13191
 
4.0%
h 10641
 
3.2%
Other values (87) 120948
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
55462
16.6%
e 27715
 
8.3%
t 22126
 
6.6%
o 20614
 
6.2%
a 17924
 
5.4%
n 16475
 
4.9%
i 15036
 
4.5%
s 13399
 
4.0%
r 13191
 
4.0%
h 10641
 
3.2%
Other values (87) 120948
36.3%

sporting_relatives
Text

Missing 

Distinct2517
Distinct (%)> 99.9%
Missing8595
Missing (%)77.3%
Memory size86.9 KiB
2025-03-12T16:36:03.448477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length981
Median length495
Mean length181.16322
Min length22

Characters and Unicode

Total characters456169
Distinct characters100
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2516 ?
Unique (%)99.9%

Sample

1st rowUncle, Roman Amoyan (wrestling), 2008 Olympic bronze medallist and two-time European champion in Greco-Roman
2nd rowUncle, Salvador "Chava" Sabrino (diving), finished 11th in the 10m platform event at the 1980 Olympic Games and is head coach for the Australian national team
3rd rowPartner, Iker Casas (taekwondo), won a bronze medal at the 2018 Central American and Caribbean Games (63kg) and a silver at the 2021 Pan American Championships (68kg)
4th rowFather, Manuel Verde (boxing), competed for Mexico in the men's light heavyweight division at the Barcelona 1992 Olympic Games
5th rowBrother Jair (athletics), 2024 National Championships high jump silver medallist
ValueCountFrequency (%)
the 3157
 
4.9%
in 2729
 
4.2%
at 1957
 
3.0%
and 1498
 
2.3%
for 982
 
1.5%
world 909
 
1.4%
championships 747
 
1.2%
olympic 697
 
1.1%
games 689
 
1.1%
a 685
 
1.1%
Other values (9228) 50782
78.3%
2025-03-12T16:36:03.585382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62498
 
13.7%
e 36702
 
8.0%
a 31259
 
6.9%
i 26127
 
5.7%
n 25585
 
5.6%
t 24974
 
5.5%
r 22480
 
4.9%
o 21875
 
4.8%
l 18555
 
4.1%
s 18031
 
4.0%
Other values (90) 168083
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 456169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
62498
 
13.7%
e 36702
 
8.0%
a 31259
 
6.9%
i 26127
 
5.7%
n 25585
 
5.6%
t 24974
 
5.5%
r 22480
 
4.9%
o 21875
 
4.8%
l 18555
 
4.1%
s 18031
 
4.0%
Other values (90) 168083
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 456169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
62498
 
13.7%
e 36702
 
8.0%
a 31259
 
6.9%
i 26127
 
5.7%
n 25585
 
5.6%
t 24974
 
5.5%
r 22480
 
4.9%
o 21875
 
4.8%
l 18555
 
4.1%
s 18031
 
4.0%
Other values (90) 168083
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 456169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
62498
 
13.7%
e 36702
 
8.0%
a 31259
 
6.9%
i 26127
 
5.7%
n 25585
 
5.6%
t 24974
 
5.5%
r 22480
 
4.9%
o 21875
 
4.8%
l 18555
 
4.1%
s 18031
 
4.0%
Other values (90) 168083
36.8%

ritual
Text

Missing 

Distinct847
Distinct (%)98.8%
Missing10256
Missing (%)92.3%
Memory size86.9 KiB
2025-03-12T16:36:03.690366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length857
Median length291
Mean length124.78646
Min length7

Characters and Unicode

Total characters106942
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)97.9%

Sample

1st rowIn competition she wears a ring or earrings her family gave her
2nd rowWhen she is preparing for a competition, applying her makeup and combing her hair, she watches the film Hercules. "Once I’m ready I listen to music that makes me dance a little.” (intlgymnast.com)
3rd rowShe is a Buddhist
4th row"I believe a lot in energies and collaboration with the universe. I give thanks to God in advance for my future success." (Athlete, 1 Jul 2024)
5th rowBefore competition he listens to ‘Stardust Crusaders’ theme from JoJo's Bizarre Adventure (manga). During a race he has Vivaldi's 'The Four Seasons' in his head
ValueCountFrequency (%)
to 789
 
4.1%
the 742
 
3.8%
a 596
 
3.1%
i 593
 
3.1%
and 581
 
3.0%
before 404
 
2.1%
her 300
 
1.5%
in 287
 
1.5%
on 252
 
1.3%
my 250
 
1.3%
Other values (2968) 14610
75.3%
2025-03-12T16:36:03.889644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18553
17.3%
e 10146
 
9.5%
t 7484
 
7.0%
a 6599
 
6.2%
o 6337
 
5.9%
i 5426
 
5.1%
s 5380
 
5.0%
n 5286
 
4.9%
r 4702
 
4.4%
h 4167
 
3.9%
Other values (79) 32862
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18553
17.3%
e 10146
 
9.5%
t 7484
 
7.0%
a 6599
 
6.2%
o 6337
 
5.9%
i 5426
 
5.1%
s 5380
 
5.0%
n 5286
 
4.9%
r 4702
 
4.4%
h 4167
 
3.9%
Other values (79) 32862
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18553
17.3%
e 10146
 
9.5%
t 7484
 
7.0%
a 6599
 
6.2%
o 6337
 
5.9%
i 5426
 
5.1%
s 5380
 
5.0%
n 5286
 
4.9%
r 4702
 
4.4%
h 4167
 
3.9%
Other values (79) 32862
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18553
17.3%
e 10146
 
9.5%
t 7484
 
7.0%
a 6599
 
6.2%
o 6337
 
5.9%
i 5426
 
5.1%
s 5380
 
5.0%
n 5286
 
4.9%
r 4702
 
4.4%
h 4167
 
3.9%
Other values (79) 32862
30.7%

other_sports
Text

Missing 

Distinct1046
Distinct (%)98.7%
Missing10053
Missing (%)90.5%
Memory size86.9 KiB
2025-03-12T16:36:03.984179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length655
Median length243
Mean length109.41887
Min length6

Characters and Unicode

Total characters115984
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1038 ?
Unique (%)97.9%

Sample

1st rowCompeted in marathon swimming at London 2012. Finished 20th in the 10km. She also competed in the Marathon Swimming World Cup circuit and at multiple world championships
2nd rowHe was 2019 sambo world champion at 82kg (held in Cheongju, Republic of Korea)
3rd rowFootball: Plays as a striker for the Malawi national team. Won the 2023 COSAFA Women's Championship final
4th rowEquestrian jumping: Represented Singapore at international youth level and competed in the individual and team events at the 2010 Youth Olympic Games in Singapore
5th rowKurash: Silver medallist at the 2016 World Junior Championships
ValueCountFrequency (%)
in 1203
 
6.6%
the 1095
 
6.0%
at 694
 
3.8%
and 500
 
2.7%
championships 288
 
1.6%
world 286
 
1.6%
competed 260
 
1.4%
for 251
 
1.4%
played 250
 
1.4%
a 249
 
1.4%
Other values (2415) 13194
72.2%
2025-03-12T16:36:04.116307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17215
14.8%
e 9268
 
8.0%
a 8144
 
7.0%
n 7557
 
6.5%
i 6993
 
6.0%
t 6495
 
5.6%
o 6007
 
5.2%
l 4877
 
4.2%
s 4821
 
4.2%
r 4414
 
3.8%
Other values (78) 40193
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17215
14.8%
e 9268
 
8.0%
a 8144
 
7.0%
n 7557
 
6.5%
i 6993
 
6.0%
t 6495
 
5.6%
o 6007
 
5.2%
l 4877
 
4.2%
s 4821
 
4.2%
r 4414
 
3.8%
Other values (78) 40193
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17215
14.8%
e 9268
 
8.0%
a 8144
 
7.0%
n 7557
 
6.5%
i 6993
 
6.0%
t 6495
 
5.6%
o 6007
 
5.2%
l 4877
 
4.2%
s 4821
 
4.2%
r 4414
 
3.8%
Other values (78) 40193
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17215
14.8%
e 9268
 
8.0%
a 8144
 
7.0%
n 7557
 
6.5%
i 6993
 
6.0%
t 6495
 
5.6%
o 6007
 
5.2%
l 4877
 
4.2%
s 4821
 
4.2%
r 4414
 
3.8%
Other values (78) 40193
34.7%

Interactions

2025-03-12T16:35:56.210012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.057138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.136544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.237592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.085209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.161419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.262222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.111145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:56.184471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-12T16:36:04.138553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
codecurrentfunctiongenderheightweight
code1.0000.0000.0000.0000.0730.027
current0.0001.0000.0000.0051.0001.000
function0.0000.0001.0000.0050.0040.000
gender0.0000.0050.0051.0000.3490.141
height0.0731.0000.0040.3491.0000.149
weight0.0271.0000.0000.1410.1491.000

Missing values

2025-03-12T16:35:56.323925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T16:35:56.406666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T16:35:56.591576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

codecurrentnamename_shortname_tvgenderfunctioncountry_codecountrycountry_longnationalitynationality_longnationality_codeheightweightdisciplineseventsbirth_datebirth_placebirth_countryresidence_placeresidence_countrynicknamehobbiesoccupationeducationfamilylangcoachreasonheroinfluencephilosophysporting_relativesritualother_sports
01532872TrueALEKSANYAN ArturALEKSANYAN AArtur ALEKSANYANMaleAthleteARMArmeniaArmeniaArmeniaArmeniaARM0.00.0['Wrestling']["Men's Greco-Roman 97kg"]1991-10-21GYUMRIArmeniaGYUMRIArmeniaWhite BearPlaying and watching footballAthleteGraduated from Shirak State University (Gyumri, ARM)Father, Gevorg AleksanyanArmenian, English, RussianGevorg Aleksanyan (ARM), fatherHe followed his father and his uncle into the sportFootballer Zinedine Zidane (FRA), World Cup winner (1998) and European champion (2000) with France, won the Champions League as a player and three times as a manager with Real Madrid, three-time FIFA World Player of the YearHis father, Gevorg Aleksanyan"Wrestling is my life." (mediamax.am. 18 May 2016)NaNNaNNaN
11532873TrueAMOYAN MalkhasAMOYAN MMalkhas AMOYANMaleAthleteARMArmeniaArmeniaArmeniaArmeniaARM0.00.0['Wrestling']["Men's Greco-Roman 77kg"]1999-01-22YEREVANArmeniaYEREVANArmeniaNaNNaNNaNNaNNaNArmenianNaNNaNNaNNaN"To become a good athlete, you first have to be a good person." (ankakh.com, 6 Oct 2018)Uncle, Roman Amoyan (wrestling), 2008 Olympic bronze medallist and two-time European champion in Greco-RomanNaNNaN
21532874TrueGALSTYAN SlavikGALSTYAN SSlavik GALSTYANMaleAthleteARMArmeniaArmeniaArmeniaArmeniaARM0.00.0['Wrestling']["Men's Greco-Roman 67kg"]1996-12-21NaNNaNYEREVANArmeniaNaNNaNNaNNaNNaNArmenianPersonal: Martin Alekhanyan (ARM).<br>National: Armen Babalaryan (ARM)NaNNaNNaNNaNNaNNaNNaN
31532944TrueHARUTYUNYAN ArsenHARUTYUNYAN AArsen HARUTYUNYANMaleAthleteARMArmeniaArmeniaArmeniaArmeniaARM0.00.0['Wrestling']["Men's Freestyle 57kg"]1999-11-22MASISArmeniaYEREVANArmeniaNaNNaNAthleteGraduated with a Master's degree from the Armenian State Institute of Physical Culture and Sport (2023)Wife, Diana (married October 2022). Daughter, Marias (born 2023)ArmenianNational: Habetnak KurghinyanWhile doing karate he noticed wrestlers training and decided to give it a try. He also tried judo but his father, a former wrestler, did not allow him to do both, so he chose wrestling. (sport.mediamax.am, 10 July 2017)Wrestler Armen Nazaryan (ARM, BUL), two-time Olympic champion (1996, 2000) and 2004 bronze medallist. Eight-time world championship medallist (three gold, two silver, three bronze)NaN“Nothing is impossible, set goals in front of you, fight and achieve it.” (Instagram, 13 May 2023)NaNNaNNaN
41532945TrueTEVANYAN VazgenTEVANYAN VVazgen TEVANYANMaleAthleteARMArmeniaArmeniaArmeniaArmeniaARM0.00.0['Wrestling']["Men's Freestyle 65kg"]1999-10-27POKR VEDIArmeniaNaNArmeniaNaNNaNAthleteStudied at the Armenian State Institute of Physical Culture and Sport (Yerevan, ARM)Wife, Sona (married November 2023)Armenian, RussianNational: Habetnak Kurghinyan (ARM)“My family did not like wrestling very much. At first I wanted to do boxing but my older friends advised me to go to wrestling training, and after a week, I started to like the sport.” (myInfo)NaNNaNNaNNaNNaNNaN
51532951TrueARENAS LorenaARENAS LLorena ARENASFemaleAthleteCOLColombiaColombiaColombiaColombiaCOL162.00.0['Athletics']["Women's 20km Race Walk", 'Marathon Race Walk Relay Mixed']1993-09-17PEREIRAColombiaMELBOURNEAustraliaNaNNaNAthletePhysical Education at Jaime Isaza Cadavid Colombian Polytechnic, Medellin (COL)Father, Jose Otoniel. Mother, Maria Rudy. Has three siblingsSpanishPersonal: Brent Vallance (AUS)NaNRace walker Luis Fernando Lopez (COL), four-time Olympian (2004, 2008, 2012, 2016), 2011 world champion (20km walk)NaNNaNNaNIn competition she wears a ring or earrings her family gave herNaN
61533112TrueMcKENZIE AshleyMcKENZIE AAshley McKENZIEMaleAthleteJAMJamaicaJamaicaJamaicaJamaicaJAM0.00.0['Judo']['Men -60 kg']1989-07-17LONDONGreat BritainCAMBERLEYGreat BritainBad BoyMusicAthleteNaNOne daughter, Lana-Rose, who lives in FranceEnglishPersonal and National: Luke Preston (GBR)"I was thrown over [an argument about] a Pokemon card and wanted to learn to throw immediately." (Athlete, 25 Jun 2024)Boxer Muhammad Ali, born Cassius Clay (USA), former undisputed heavyweight champion of the world, 1960 Olympic champion (light heavyweight), nicknamed 'The Greatest' and regarded as one of the most significant sports figures of the 20th century"My coach Luke Preston. We've been a team for the last 12 years." (Athlete, 25 Jun 2024)NaNNaNNaNNaN
71533136TrueBASS BITTAYE Gina MariamBASS BITTAYE GMGina Mariam BASS BITTAYEFemaleAthleteGAMGambiaGambiaGambiaGambiaGAM161.00.0['Athletics']["Women's 100m", "Women's 200m"]1995-05-03TUBAKUTAGambiaNaNNaNEarlier in her career she was nicknamed the 'Poor Olympian' due to the financial challenges she facedNaNAthlete, police officer (sub-inspector)NaNHusband, Mustapha Bittaye - physical education lecturer and football referee (married October 2023). One sister and two brothersEnglish, FrenchPersonal: Christophe Belliard (FRA)“I started running when I was in primary school, like we have junior championships. It's where I started running and it’s where I started to notice that, if I work hard I will be a great athlete.” (olympics.com, 24 Apr 2024)Sprinter Shelly-Ann Fraser-Pryce (JAM), three time Olympic champion (four silver, one bronze), 16-time world championship medallist (10 gold, five silver, one bronze). In the 100m, two-time Olympic champion (2008, 2012), five-time world champion (2009, 2013, 2015, 2019, 2022). </p><p>"Shelly-Ann, she is consistent. She is a mother but she still loves what she is doing and she is still performing as she did before." (Tales of Hagie Drammeh Youtube, 16 Jan 2023)</p><p>Sprinter Marie-Josee Ta Lou (CIV), three fourth places at the Olympic Games (2016, 2020), double world silver medallist (100m-200m) in 2017, world bronze medallist in 2019 (100m)NaN"If you believe in yourself, never be discouraged." (worldathletics.org, 17 Dec 2019)NaNNaNNaN
81533176TrueCAMARA EbrahimaCAMARA EEbrahima CAMARAMaleAthleteGAMGambiaGambiaGambiaGambiaGAM178.00.0['Athletics']["Men's 100m"]1996-09-18BUNDUNGGambiaANGERSFranceNaNRunning, watching movies, football, chatting with friendsAthlete, prison officerNaNMarried. One daughter. Has three brothersArabic, English, French, Mandinka, WolofPersonal: Christophe Belliard (FRA)"I love running and I was fast a child." (Athlete, 7 Jul 2024)Sprinter Gina Mariam Bass Bittaye (GAM), two-time Olympian (Rio 2016, Tokyo 2020), 2019 World Championships 200m finalist, four-time African championships medallist (two gold, two bronze), four-time African Games medallist (three gold, one silver)Momodou Lamin Kujabi, a former international athlete from The Gambia who was once his physical education teacher"What does not kill you makes you stronger." (Athlete, 7 Jul 2024)NaNNaNNaN
91533188TrueRUEDA SANTOS LizethRUEDA SANTOS LLizeth RUEDA SANTOSFemaleAthleteMEXMexicoMexicoMexicoMexicoMEX0.00.0['Triathlon']["Women's Individual"]1994-03-07GUADALAJARAMexicoXALAPAMexicoNaNNaNAthleteStudied Medicine at the University of Guadalajara (MEX)NaNSpanishPersonal: Eugenio Chimal (MEX)Was a competitive swimmer from age 12 and went on to race internationally in marathon swimmingNaNNaNNaNNaNNaNCompeted in marathon swimming at London 2012. Finished 20th in the 10km. She also competed in the Marathon Swimming World Cup circuit and at multiple world championships
codecurrentnamename_shortname_tvgenderfunctioncountry_codecountrycountry_longnationalitynationality_longnationality_codeheightweightdisciplineseventsbirth_datebirth_placebirth_countryresidence_placeresidence_countrynicknamehobbiesoccupationeducationfamilylangcoachreasonheroinfluencephilosophysporting_relativesritualother_sports
111034979790TrueINSIXIENGMAY StevenINSIXIENGMAY SSteven INSIXIENGMAYMaleAthleteLAOLao PDRLao People's Democratic RepublicLao PDRLao People's Democratic RepublicLAO0.00.0['Swimming']["Men's 100m Breaststroke"]2004-01-21NaNNaNWINSTON-SALEM, NCUnited StatesNaNNaNAthlete, studentStudying Business at the University of Georgia (Athens, GA, USA)Father, Dalavong. Mother, Sengphet SayaphanthongEnglishNaNNaNNaNNaNNaNNaNNaNNaN
111044980004Truevan de WIEL Annevan de WIEL AAnne van de WIELFemaleAthleteNEDNetherlandsNetherlandsNetherlandsNetherlandsNED168.00.0['Athletics']["Women's 4 x 400m Relay"]1997-06-04NaNNaNROTTERDAMNetherlandsNaNNaNAthlete, studentSociology - Erasmus University Rotterdam, NetherlandsNaNDutch, EnglishNational: Laurent Meuwly.<br>Personal: Niels HanegraafShe got involved in athletics along with her twin sister Myke. "We were pretty boisterous kids. An uncle, whose two sons did athletics, suggested to our mother that we join an athletics club. We enjoyed it so much that we're still at it today." (erasmusmagazine.nl, 6 Jun 2018)NaNNaNNaNSister, Myke van de Wiel (athletics), is a two-time Dutch national champion in the heptathlon (2018, 2021) and won an indoor national title in the 200m in 2021NaNNaN
111054982175TrueJOSEPH ElijahJOSEPH EElijah JOSEPHMaleAthleteTTOTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTTO177.00.0['Athletics']["Men's 4 x 400m Relay"]2001-07-03NaNNaNNaNNaNNaNNaNAthleteNaNNaNEnglishPersonal: Ebert Joseph (TTO)NaNNaNNaNNaNNaNNaNNaN
111064982762TrueHOMAN KhrystynaHOMAN KKhrystyna HOMANFemaleAthleteUKRUkraineUkraineUkraineUkraineUKR0.00.0['Judo']['Women +78 kg']1999-01-31SLAVUTYCHUkraineKIEVUkraineDangerPlaying the guitar, eating delicious food, playing basketballAthleteGraduated with a Bachelor's degree in Physical Culture and Sports in December 2021Mother and one younger brotherUkrainianNational: Quedjau Nhabali (UKR).<br>Personal: Oleg Kopylov (UKR).<br>Club: Sergey Dubrova (UKR)Because her mother raised the ideaNaN"Quedjau Nhabali is always there with the right words. Oleg Kopylov is the first coach whose opinion is important to me." (Athlete, 9 Jul 2024)NaNNaNNaNShe likes MMA
111074983537TrueCHELANGAT Annet ChemengichCHELANGAT ACAnnet Chemengich CHELANGATFemaleAthleteUGAUgandaUgandaUgandaUgandaUGA174.00.0['Athletics']["Women's 10,000m"]1993-07-29NaNUgandaNaNUgandaNaNNaNAthleteNaNNaNEnglishNaNNaNNaNNaNNaNNaNNaNNaN
111084986655TrueADA ETO SeforaADA ETO SSefora ADA ETOFemaleAthleteGEQEquatorial GuineaEquatorial GuineaEquatorial GuineaEquatorial GuineaGEQ165.00.0['Athletics']["Women's 100m"]2003-06-11ABIERE ESATOP, NSOK NSOMOEquatorial GuineaMALABOEquatorial GuineaNaNNaNNaNNaNNaNSpanishPersonal: Feliciano Javier y Mananses Mba MichaNaNNaNNaNNaNNaNNaNNaN
111099460001TrueLIUZZI EmanuelaLIUZZI EEmanuela LIUZZIFemaleAthleteITAItalyItalyNaNNaNNaN0.00.0['Wrestling']["Women's Freestyle 50kg"]2000-04-27NaNNaNNaNNaNNaNNaNAthleteNaNNaNItalianNaNNaNNaNNaNNaNNaNNaNNaN
111101972077FalseBOERS IsayahNaNNaNMaleAthleteNEDNetherlandsNetherlandsNetherlandsNetherlandsNEDNaNNaN[Athletics][4 x 400m Relay Mixed]1999-06-19NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
111111899865FalseSTAUT KevinNaNNaNMaleAthleteFRAFranceFranceFranceFranceFRANaNNaN[Equestrian][Jumping Team]1980-11-15NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
111121924402FalseCARVELL CharlieNaNNaNMaleAthleteGBRGreat BritainGreat BritainGreat BritainGreat BritainGBRNaNNaN[Athletics][Men's 4 x 400m Relay]2004-06-30NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN